用T5ProtChem对分子和蛋白质语言表示的统一深度学习
Thomas Kelly1, Song Xia1, Jieyu Lu1
1Department of Chemistry, New York University, New York, New York 10003, United States.
Journal of chemical information and modeling
|April 8, 2025
概括
一个新的统一深度学习模型,T5ProtChem,同时处理分子和蛋白质序列. 这种方法通过实现结合和反应预测的最先进结果来增强药物发现和蛋白质工程.
科学领域:
- 计算生物学是一种计算生物学.
- 计算化学是一种计算化学.
- 生命科学中的人工智能
背景情况:
- 深度学习模型经常单独处理分子和蛋白质数据,限制它们利用共享原子原理的能力.
- 现有的架构未能充分利用分子和蛋白质序列的相互联系.
研究的目的:
- 推出T5ProtChem,一个统一的深度学习模型,能够同时处理分子和蛋白质序列.
- 开发一个新的预训练目标,ProtiSMILES,它弥合了分子和蛋白质领域之间的差距,用于增强的建模.
主要方法:
- 使用T5架构为统一的序列处理模型.
- 实现ProtiSMILES预训练目标,整合分子和蛋白质数据.
- 在各种任务上评估T5ProtChem,包括结合亲和力,反应预测和蛋白质功能预测.
主要成果:
- 在结合亲和和反应预测任务中,T5ProtChem实现了最先进的性能.
- 该模型在蛋白质功能预测方面表现出强的表现.
- 支持新型应用,如共价结合剂分类和序列级 adduct 预测.
结论:
- 统一的语言模型为药物发现和蛋白质工程中的跨学科应用提供了一种多功能方法.
- T5ProtChem展示了集成建模的力量,用于推进计算生物学和化学.
- 这种统一的框架促进了高效和可通用的蛋白质化学建模.
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